An Intelligent System for the Heatsink Design

  • Yao-Wen Hsueh
  • Hsin-Chung Lien
  • Ming-Hsien Hsueh
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3973)


Via two-stage back-propagation neural network (BNN) learning algorithm, this paper establishes the relationship between different heatsink design parameters and performance evaluation, and induces 5 corresponding performance outputs from 6 different heatsink design and operating condition parameters (inlet airflow velocity, heatsink length or width, fin thickness, fin gap, fin height and heatsink base height) by using Computation Fluid Dynamics (CFD). After two stages well-trained, the BNN model with error compensator is able to accurate estimate the output values under different heatsink design and operation conditions.


Error Compensator Airflow Velocity Computation Fluid Dynamics Chip Package Operating Condition Parameter 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Yao-Wen Hsueh
    • 1
  • Hsin-Chung Lien
    • 1
  • Ming-Hsien Hsueh
    • 1
  1. 1.Department of Mechanical EngineeringNorthern Taiwan Institute of Science and TechnologyTaipeiTaiwan, ROC

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